# Bio.motifs.thresholds module

Approximate calculation of appropriate thresholds for motif finding.

class Bio.motifs.thresholds.ScoreDistribution(motif=None, precision=10**3, pssm=None, background=None)

  Bases: `object`

  Class representing approximate score distribution for a given motif.

  Utilizes a dynamic programming approach to calculate the distribution of scores with a predefined precision. Provides a number of methods for calculating thresholds for motif occurrences.

  __init__(motif=None, precision=10**3, pssm=None, background=None)

    Initialize the class.

  __firstlineno__ = 9
  __static_attributes__ = ('bg_density', 'ic', 'interval', 'min_score', 'mo_density', 'n_points', 'step')
  modify(scores, mo_probs, bg_probs)

    Modify motifs and background density.

  threshold_fpr(fpr)

    Approximate the log-odds threshold which makes the type I error (false positive rate).

  threshold_fnr(fnr)

    Approximate the log-odds threshold which makes the type II error (false negative rate).

  threshold_balanced(rate_proportion=1.0, return_rate=False)

    Approximate log-odds threshold making FNR equal to FPR times rate_proportion.

  threshold_patser()

    Threshold selection mimicking the behaviour of patser (Hertz, Stormo 1999) software.

    It selects such a threshold that the log(fpr)=-ic(M) note: the actual patser software uses natural logarithms instead of log_2, so the numbers are not directly comparable.

Biopython v: 1.85

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